Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)

Recently, the advancements of Internet-of-Things (IoT) have expanded its application in underwater environment which leads to the development of a new field of Internet of Underwater Things (IoUT). It offers a broader view of applications such as atmosphere observation, habitat monitoring of sea ani...

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Published in:	Journal of real-time image processing Vol. 17; no. 6; pp. 2097 - 2111
Main Authors:	Krishnaraj, N., Elhoseny, Mohamed, Thenmozhi, M., Selim, Mahmoud M., Shankar, K.
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020 Springer Nature B.V
Subjects:	Algorithms Artificial neural networks Coding standards Communication Computer Graphics Computer Science Data compression Data transmission Deep learning Discrete Wavelet Transform Energy efficiency Image coding Image compression Image Processing and Computer Vision Image quality Image reconstruction Image transmission Internet of Things Machine learning Multimedia Information Systems Neural networks Pattern Recognition Propagation Sensors Signal to noise ratio Signal,Image and Speech Processing Special Issue Paper Underwater Deep learning Image compression Image reconstruction IoUT Underwater
ISSN:	1861-8200, 1861-8219
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Abstract	Recently, the advancements of Internet-of-Things (IoT) have expanded its application in underwater environment which leads to the development of a new field of Internet of Underwater Things (IoUT). It offers a broader view of applications such as atmosphere observation, habitat monitoring of sea animals, defense and disaster prediction. Data transmission of images captured by the smart underwater objects is very challenging due to the nature of underwater environment and necessitates an efficient image transmission strategy for IoUT. In this paper, we model and implement a discrete wavelet transform (DWT) based deep learning model for image compression in IoUT. For achieving effective compression with better reconstruction image quality, convolution neural network (CNN) is used at the encoding as well as decoding side. We validate DWT–CNN model using extensive set of experimentations and depict that the presented deep learning model is superior to existing methods such as super-resolution convolutional neural networks (SRCNN), JPEG and JPEG2000 in terms of compression performance as well as reconstructed image quality. The DWT–CNN model attains an average peak signal-to-noise ratio (PSNR) of 53.961 with average space saving (SS) of 79.7038%.
AbstractList	Recently, the advancements of Internet-of-Things (IoT) have expanded its application in underwater environment which leads to the development of a new field of Internet of Underwater Things (IoUT). It offers a broader view of applications such as atmosphere observation, habitat monitoring of sea animals, defense and disaster prediction. Data transmission of images captured by the smart underwater objects is very challenging due to the nature of underwater environment and necessitates an efficient image transmission strategy for IoUT. In this paper, we model and implement a discrete wavelet transform (DWT) based deep learning model for image compression in IoUT. For achieving effective compression with better reconstruction image quality, convolution neural network (CNN) is used at the encoding as well as decoding side. We validate DWT–CNN model using extensive set of experimentations and depict that the presented deep learning model is superior to existing methods such as super-resolution convolutional neural networks (SRCNN), JPEG and JPEG2000 in terms of compression performance as well as reconstructed image quality. The DWT–CNN model attains an average peak signal-to-noise ratio (PSNR) of 53.961 with average space saving (SS) of 79.7038%.
Author	Selim, Mahmoud M. Elhoseny, Mohamed Thenmozhi, M. Shankar, K. Krishnaraj, N.
Author_xml	– sequence: 1 givenname: N. surname: Krishnaraj fullname: Krishnaraj, N. organization: Department of Computer Science and Engineering, SASI Institute of Technology and Engineering – sequence: 2 givenname: Mohamed surname: Elhoseny fullname: Elhoseny, Mohamed organization: Faculty of Computers and Information, Mansoura University – sequence: 3 givenname: M. surname: Thenmozhi fullname: Thenmozhi, M. organization: Department of IT, SRM Institute of Science and Technology – sequence: 4 givenname: Mahmoud M. surname: Selim fullname: Selim, Mahmoud M. organization: Department of Mathematics, Al-Aflaj College of Science and Human Studies, Prince Sattam Bin Abdulaziz University – sequence: 5 givenname: K. surname: Shankar fullname: Shankar, K. email: shankarcrypto@gmail.com organization: School of Computing, Kalasalingam Academy of Research and Education
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SubjectTerms	Algorithms Artificial neural networks Coding standards Communication Computer Graphics Computer Science Data compression Data transmission Deep learning Discrete Wavelet Transform Energy efficiency Image coding Image compression Image Processing and Computer Vision Image quality Image reconstruction Image transmission Internet of Things Machine learning Multimedia Information Systems Neural networks Pattern Recognition Propagation Sensors Signal to noise ratio Signal,Image and Speech Processing Special Issue Paper Underwater
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Title	Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)
URI	https://link.springer.com/article/10.1007/s11554-019-00879-6 https://www.proquest.com/docview/2918676897
Volume	17
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